# Marker-free Human Gait Analysis using a Smart Edge Sensor System

arXiv: https://arxiv.org/abs/2411.09538 · HTML: https://mlsanity.com/paper/2411.09538/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 30 references that are in the map.
- First posted on arXiv 14 Nov 2024.

## Builds on (most cited first, 10 of 10)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [In Defense of the Triplet Loss for Person Re-Identification](https://mlsanity.com/paper/1703.07737/) (2017)
- [Direct Multi-view Multi-person 3D Pose Estimation](https://mlsanity.com/paper/2111.04076/) (2021)
- [Faster VoxelPose: Real-time 3D Human Pose Estimation by Orthographic Projection](https://mlsanity.com/paper/2207.10955/) (2022)
- [Generalizable Human Pose Triangulation](https://mlsanity.com/paper/2110.00280/) (2021)
- [Explaining the Unique Nature of Individual Gait Patterns with Deep Learning](https://mlsanity.com/paper/1808.04308/) (2018)
- [Real-Time Multi-View 3D Human Pose Estimation using Semantic Feedback to Smart Edge Sensors](https://mlsanity.com/paper/2106.14729/) (2021)
- [Online Marker-free Extrinsic Camera Calibration using Person Keypoint Detections](https://mlsanity.com/paper/2209.07393/) (2022)
- [3D Semantic Scene Perception using Distributed Smart Edge Sensors](https://mlsanity.com/paper/2205.01460/) (2022)

## Abstract (arXiv)

The human gait is a complex interplay between the neuronal and the muscular systems, reflecting an individual's neurological and physiological condition. This makes gait analysis a valuable tool for biomechanics and medical experts. Traditional observational gait analysis is cost-effective but lacks reliability and accuracy, while instrumented gait analysis, particularly using marker-based optical systems, provides accurate data but is expensive and time-consuming. In this paper, we introduce a novel markerless approach for gait analysis using a multi-camera setup with smart edge sensors to estimate 3D body poses without fiducial markers. We propose a Siamese embedding network with triplet loss calculation to identify individuals by their gait pattern. This network effectively maps gait sequences to an embedding space that enables clustering sequences from the same individual or activity closely together while separating those of different ones. Our results demonstrate the potential of the proposed system for efficient automated gait analysis in diverse real-world environments, facilitating a wide range of applications.